


"""simple docstring"""



from typing import Tuple, Union

from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig


if is_timm_available():
		import timm


if is_torch_available():
		from torch import Tensor







class    __a						( __snake_case					,			__snake_case							):
				lowerCamelCase     :       Tuple			       ='pixel_values'
				lowerCamelCase     :       Optional[int]			       =False
				lowerCamelCase     :       Optional[Any]			       =TimmBackboneConfig







				def __init__( self      ,	UpperCAmelCase      ,	**UpperCAmelCase   ):

					'''simple docstring'''






					requires_backends(self      ,	'''timm'''   )
					super().__init__(UpperCAmelCase   )
					lowerCAmelCase_      							=			config

					if config.backbone is None:
						raise ValueError('''backbone is not set in the config. Please set it to a timm model name.'''   )

					if config.backbone not in timm.list_models():
						raise ValueError(F"""backbone {config.backbone} is not supported by timm."""   )

					if hasattr(UpperCAmelCase      ,	'''out_features'''   ) and config.out_features is not None:
						raise ValueError('''out_features is not supported by TimmBackbone. Please use out_indices instead.'''   )

					lowerCAmelCase_      							=			getattr(UpperCAmelCase      ,	'''use_pretrained_backbone'''      ,	UpperCAmelCase   )
					if pretrained is None:
						raise ValueError('''use_pretrained_backbone is not set in the config. Please set it to True or False.'''   )

					# We just take the final layer by default. This matches the default for the transformers models.
					lowerCAmelCase_      							=			config.out_indices if getattr(UpperCAmelCase      ,	'''out_indices'''      ,	UpperCAmelCase   ) is not None else (-1,)

					lowerCAmelCase_      							=			timm.create_model(
					    config.backbone      ,	pretrained=UpperCAmelCase      ,	features_only=config.features_only      ,	in_chans=config.num_channels      ,	out_indices=UpperCAmelCase      ,	**UpperCAmelCase      ,	)
					# These are used to control the output of the model when called. If output_hidden_states is True, then
					# return_layers is modified to include all layers.
					lowerCAmelCase_      							=			self._backbone.return_layers
					lowerCAmelCase_      							=			{layer['''module''']: str(UpperCAmelCase   ) for i, layer in enumerate(self._backbone.feature_info.info   )}
					super()._init_backbone(UpperCAmelCase   )







				@classmethod
				def      lowerCamelCase_     ( cls      ,	UpperCAmelCase      ,	*UpperCAmelCase      ,	**UpperCAmelCase   ):

					'''simple docstring'''






					requires_backends(cls      ,	['''vision''', '''timm''']   )
					from ...models.timm_backbone import TimmBackboneConfig

					lowerCAmelCase_      							=			kwargs.pop('''config'''      ,	TimmBackboneConfig()   )

					lowerCAmelCase_      							=			kwargs.pop('''use_timm_backbone'''      ,	UpperCAmelCase   )
					if not use_timm:
						raise ValueError('''use_timm_backbone must be True for timm backbones'''   )

					lowerCAmelCase_      							=			kwargs.pop('''num_channels'''      ,	config.num_channels   )
					lowerCAmelCase_      							=			kwargs.pop('''features_only'''      ,	config.features_only   )
					lowerCAmelCase_      							=			kwargs.pop('''use_pretrained_backbone'''      ,	config.use_pretrained_backbone   )
					lowerCAmelCase_      							=			kwargs.pop('''out_indices'''      ,	config.out_indices   )
					lowerCAmelCase_      							=			TimmBackboneConfig(
					    backbone=UpperCAmelCase      ,	num_channels=UpperCAmelCase      ,	features_only=UpperCAmelCase      ,	use_pretrained_backbone=UpperCAmelCase      ,	out_indices=UpperCAmelCase      ,	)
					return super()._from_config(UpperCAmelCase      ,	**UpperCAmelCase   )







				def      lowerCamelCase_     ( self      ,	UpperCAmelCase   ):

					'''simple docstring'''






					pass







				def      lowerCamelCase_     ( self      ,	UpperCAmelCase      ,	UpperCAmelCase=None      ,	UpperCAmelCase=None      ,	UpperCAmelCase=None      ,	**UpperCAmelCase   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			return_dict if return_dict is not None else self.config.use_return_dict
					lowerCAmelCase_      							=			(
					    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
					)
					lowerCAmelCase_      							=			output_attentions if output_attentions is not None else self.config.output_attentions

					if output_attentions:
						raise ValueError('''Cannot output attentions for timm backbones at the moment'''   )

					if output_hidden_states:
						# We modify the return layers to include all the stages of the backbone
						lowerCAmelCase_      							=			self._all_layers
						lowerCAmelCase_      							=			self._backbone(UpperCAmelCase      ,	**UpperCAmelCase   )
						lowerCAmelCase_      							=			self._return_layers
						lowerCAmelCase_      							=			tuple(hidden_states[i] for i in self.out_indices   )
					else:
						lowerCAmelCase_      							=			self._backbone(UpperCAmelCase      ,	**UpperCAmelCase   )
						lowerCAmelCase_      							=			None

					lowerCAmelCase_      							=			tuple(UpperCAmelCase   )
					lowerCAmelCase_      							=			tuple(UpperCAmelCase   ) if hidden_states is not None else None

					if not return_dict:
						lowerCAmelCase_      							=			(feature_maps,)
						if output_hidden_states:
							lowerCAmelCase_      							=			output + (hidden_states,)
						return output

					return BackboneOutput(feature_maps=UpperCAmelCase      ,	hidden_states=UpperCAmelCase      ,	attentions=UpperCAmelCase   )



"""simple docstring"""



import argparse
from pathlib import Path

import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version

from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('0.12.2'):
		raise Exception('requires fairseq >= 0.12.2')
if version.parse(fairseq.__version__) > version.parse('2'):
		raise Exception('requires fairseq < v2')

logging.set_verbosity_info()
lowercase_							=							logging.get_logger(__name__)

lowercase_							=							'Hello, World!'
lowercase_							=							'en_XX'
def      UpperCAmelCase     (       _lowercase :   str			,   _lowercase :   str			,   _lowercase :   bool	)						-> Optional[int]:


	"""simple docstring"""




	lowerCAmelCase_      							=			Path('''data_bin'''	)
	lowerCAmelCase_      							=			FairseqXmodModel.from_pretrained(
	    model_name_or_path=str(Path(_lowercase	).parent	)			,   checkpoint_file=Path(_lowercase	).name			,   _name='''xmod_base'''			,   arch='''xmod_base'''			,   task='''multilingual_masked_lm'''			,   data_name_or_path=str(_lowercase	)			,   bpe='''sentencepiece'''			,   sentencepiece_model=str(Path(_lowercase	).parent / '''sentencepiece.bpe.model'''	)			,   src_dict=str(data_dir / '''dict.txt'''	)			,   )
	xmod.eval()  # disable dropout
	print(_lowercase	)

	lowerCAmelCase_      							=			xmod.model.encoder.sentence_encoder
	lowerCAmelCase_      							=			XmodConfig(
	    vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings			,   hidden_size=xmod.cfg.model.encoder_embed_dim			,   num_hidden_layers=xmod.cfg.model.encoder_layers			,   num_attention_heads=xmod.cfg.model.encoder_attention_heads			,   intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim			,   max_position_embeddings=5_1_4			,   type_vocab_size=1			,   layer_norm_eps=1E-5			,   pre_norm=xmod.cfg.model.encoder_normalize_before			,   adapter_reduction_factor=getattr(xmod.cfg.model			,   '''bottleneck'''			,   2	)			,   adapter_layer_norm=xmod.cfg.model.adapter_layer_norm			,   adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm			,   ln_before_adapter=xmod.cfg.model.ln_before_adapter			,   languages=xmod.cfg.model.languages			,   )
	if classification_head:
		lowerCAmelCase_      							=			xmod.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

	print('''Our X-MOD config:'''			,   _lowercase	)

	lowerCAmelCase_      							=			XmodForSequenceClassification(_lowercase	) if classification_head else XmodForMaskedLM(_lowercase	)
	model.eval()

	# Now let's copy all the weights.
	# Embeddings
	lowerCAmelCase_      							=			xmod_sent_encoder.embed_tokens.weight
	lowerCAmelCase_      							=			xmod_sent_encoder.embed_positions.weight
	lowerCAmelCase_      							=			torch.zeros_like(
	    model.roberta.embeddings.token_type_embeddings.weight	)  # just zero them out b/c xmod doesn't use them.

	lowerCAmelCase_      							=			xmod_sent_encoder.layernorm_embedding.weight
	lowerCAmelCase_      							=			xmod_sent_encoder.layernorm_embedding.bias

	for i in range(config.num_hidden_layers	):
		# Encoder: start of layer
		lowerCAmelCase_      							=			model.roberta.encoder.layer[i]
		lowerCAmelCase_      							=			xmod_sent_encoder.layers[i]

		# self attention
		lowerCAmelCase_      							=			layer.attention.self
		if not (
		    xmod_layer.self_attn.k_proj.weight.data.shape
		    == xmod_layer.self_attn.q_proj.weight.data.shape
		    == xmod_layer.self_attn.v_proj.weight.data.shape
		    == torch.Size((config.hidden_size, config.hidden_size)	)
		):
			raise AssertionError('''Dimensions of self-attention weights do not match.'''	)

		lowerCAmelCase_      							=			xmod_layer.self_attn.q_proj.weight
		lowerCAmelCase_      							=			xmod_layer.self_attn.q_proj.bias
		lowerCAmelCase_      							=			xmod_layer.self_attn.k_proj.weight
		lowerCAmelCase_      							=			xmod_layer.self_attn.k_proj.bias
		lowerCAmelCase_      							=			xmod_layer.self_attn.v_proj.weight
		lowerCAmelCase_      							=			xmod_layer.self_attn.v_proj.bias

		# self-attention output
		lowerCAmelCase_      							=			layer.attention.output
		if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
			raise AssertionError('''Dimensions of self-attention output weights do not match.'''	)
		lowerCAmelCase_      							=			xmod_layer.self_attn.out_proj.weight
		lowerCAmelCase_      							=			xmod_layer.self_attn.out_proj.bias
		lowerCAmelCase_      							=			xmod_layer.self_attn_layer_norm.weight
		lowerCAmelCase_      							=			xmod_layer.self_attn_layer_norm.bias

		# intermediate
		lowerCAmelCase_      							=			layer.intermediate
		if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
			raise AssertionError('''Dimensions of intermediate weights do not match.'''	)
		lowerCAmelCase_      							=			xmod_layer.fca.weight
		lowerCAmelCase_      							=			xmod_layer.fca.bias

		# output
		lowerCAmelCase_      							=			layer.output
		if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
			raise AssertionError('''Dimensions of feed-forward weights do not match.'''	)
		lowerCAmelCase_      							=			xmod_layer.fca.weight
		lowerCAmelCase_      							=			xmod_layer.fca.bias
		lowerCAmelCase_      							=			xmod_layer.final_layer_norm.weight
		lowerCAmelCase_      							=			xmod_layer.final_layer_norm.bias
		if bert_output.adapter_layer_norm is not None:
			lowerCAmelCase_      							=			xmod_layer.adapter_layer_norm.weight
			lowerCAmelCase_      							=			xmod_layer.adapter_layer_norm.bias

		if sorted(bert_output.adapter_modules.keys()	) != sorted(xmod_layer.adapter_modules.keys()	):
			raise AssertionError('''Lists of language adapters do not match.'''	)
		for lang_code, adapter in xmod_layer.adapter_modules.items():
			lowerCAmelCase_      							=			bert_output.adapter_modules[lang_code]
			lowerCAmelCase_      							=			xmod_layer.adapter_modules[lang_code]
			lowerCAmelCase_      							=			from_adapter.fca.weight
			lowerCAmelCase_      							=			from_adapter.fca.bias
			lowerCAmelCase_      							=			from_adapter.fca.weight
			lowerCAmelCase_      							=			from_adapter.fca.bias

		# end of layer

	if xmod_sent_encoder.layer_norm is not None:
		lowerCAmelCase_      							=			xmod_sent_encoder.layer_norm.weight
		lowerCAmelCase_      							=			xmod_sent_encoder.layer_norm.bias

	if classification_head:
		lowerCAmelCase_      							=			xmod.model.classification_heads['''mnli'''].dense.weight
		lowerCAmelCase_      							=			xmod.model.classification_heads['''mnli'''].dense.bias
		lowerCAmelCase_      							=			xmod.model.classification_heads['''mnli'''].out_proj.weight
		lowerCAmelCase_      							=			xmod.model.classification_heads['''mnli'''].out_proj.bias
	else:
		# LM Head
		lowerCAmelCase_      							=			xmod.model.encoder.lm_head.dense.weight
		lowerCAmelCase_      							=			xmod.model.encoder.lm_head.dense.bias
		lowerCAmelCase_      							=			xmod.model.encoder.lm_head.layer_norm.weight
		lowerCAmelCase_      							=			xmod.model.encoder.lm_head.layer_norm.bias
		lowerCAmelCase_      							=			xmod.model.encoder.lm_head.weight
		lowerCAmelCase_      							=			xmod.model.encoder.lm_head.bias

	# Let's check that we get the same results.
	lowerCAmelCase_      							=			xmod.encode(_lowercase	).unsqueeze(0	)  # batch of size 1
	model.roberta.set_default_language(_lowercase	)

	lowerCAmelCase_      							=			model(_lowercase	)[0]
	if classification_head:
		lowerCAmelCase_      							=			xmod.model.classification_heads['''mnli'''](xmod.extract_features(_lowercase	)	)
	else:
		lowerCAmelCase_      							=			xmod.model(_lowercase			,   lang_id=[SAMPLE_LANGUAGE]	)[0]
	print(our_output.shape			,   their_output.shape	)
	lowerCAmelCase_      							=			torch.max(torch.abs(our_output - their_output	)	).item()
	print(F"""max_absolute_diff = {max_absolute_diff}"""	)  # ~ 1e-7
	lowerCAmelCase_      							=			torch.allclose(_lowercase			,   _lowercase			,   atol=1E-3	)
	print('''Do both models output the same tensors?'''			,   '''🔥''' if success else '''💩'''	)
	if not success:
		raise Exception('''Something went wRoNg'''	)

	Path(_lowercase	).mkdir(parents=_lowercase			,   exist_ok=_lowercase	)
	print(F"""Saving model to {pytorch_dump_folder_path}"""	)
	model.save_pretrained(_lowercase	)


if __name__ == "__main__":
		lowercase_							=							argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--xmod_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		parser.add_argument(
		    '--classification_head', action='store_true', help='Whether to convert a final classification head.'
		)
		lowercase_							=							parser.parse_args()
		convert_xmod_checkpoint_to_pytorch(
		    args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
		)